Systems and methods for processing electronic images for computational assessment of disease

ABSTRACT

Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen, determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further a cancer quantification if the cancer qualification is an confirmed cancer qualification, providing the digital image as an input to the detection machine learning model, receiving one of a pathological complete response (pCR) cancer qualification or a confirmed cancer quantification as an output from the detection machine learning model, and outputting the pCR cancer qualification or the confirmed cancer quantification.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/957,523 filed Jan. 6, 2020, the entire disclosure of which isincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally todetermining the presence or absence of disease, such as cancer cells.More specifically, particular embodiments of the present disclosurerelate to determining at least one of a pathological complete response(pCR) or a minimal residual disease (MRD) based on cells in a wholeslide image (WSI).

BACKGROUND

Pathological complete response (pCR) may refer to the absence ofresidual invasive and in situ cancer cells on histology microscopyslides of resected tissue samples. pCR may be used as a surrogateendpoint to determine whether patients are responding to therapies(e.g., therapies related to breast cancer, prostate cancer, bladdercancer, colorectal cancer, etc.). For example, pCR for breast cancer maybe defined as the lack of all signs of invasive cancer in the breasttissue and lymph nodes removed during surgery after treatment.

Minimal residual disease (MRD) may refer to minimal, such assubmicroscopic, disease such as disease that remains occult within thepatient but that may eventually lead to relapse. In cancer treatment,MRD may provide information on whether the treatment has removed thecancer or whether traces remain. Currently, pCR/MRD are determinedmanually via pathologists checking the tissue samples under a microscopeand examining whether there are still cancer cells remaining or whetherall cancer cells have been removed. This detection task may besubjective and can be challenging due to various definitions of pCR/MRDas well as treatment effects that may change the morphology of thecancerous and benign tissue due to neoadjuvant therapies. Thesubjectivity and level of challenge may increase when there is treatmentdamage.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure. The background description provided herein is for generallypresenting the context of the disclosure. Unless otherwise indicatedherein, the materials described in this section are not prior art to theclaims in this application and are not admitted to be prior art, orsuggestions of the prior art, by inclusion in this section.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for determining cancer detection results based ondigital pathology images.

A method for outputting cancer detection results includes receiving adigital image corresponding to a target specimen associated with apathology category, wherein the digital image is an image of tissuespecimen, determining a detection machine learning model, the detectionmachine learning model being generated by processing a plurality oftraining images to output a cancer qualification and further a cancerquantification if the cancer qualification is a confirmed cancerqualification, providing the digital image as an input to the detectionmachine learning model, receiving one of a pathological completeresponse (pCR) cancer qualification or a confirmed cancer quantificationas an output from the detection machine learning model, and outputtingthe pCR cancer qualification or the confirmed cancer quantification.

A system for outputting cancer detection results includes a memorystoring instructions and a processor executing the instructions toperform a process including receiving a digital image corresponding to atarget specimen associated with a pathology category, wherein thedigital image is an image of tissue specimen, determining a detectionmachine learning model, the detection machine learning model beinggenerated by processing a plurality of training images to output acancer qualification and further a cancer quantification if the cancerqualification is a confirmed cancer qualification, providing the digitalimage as an input to the detection machine learning model, receiving oneof a pathological complete response (pCR) cancer qualification or aconfirmed cancer quantification as an output from the detection machinelearning model, and outputting the pCR cancer qualification or theconfirmed cancer quantification.

A non-transitory computer-readable medium storing instructions that,when executed by processor, cause the processor to perform a method forgenerating a specialized machine learning model, the method includesreceiving a digital image corresponding to a target specimen associatedwith a pathology category, wherein the digital image is an image oftissue specimen, determining a detection machine learning model, thedetection machine learning model being generated by processing aplurality of training images to output a cancer qualification andfurther a cancer quantification if the cancer qualification is aconfirmed cancer qualification, providing the digital image as an inputto the detection machine learning model, receiving one of a pathologicalcomplete response (pCR) cancer qualification or a confirmed cancerquantification as an output from the detection machine learning model,and outputting the pCR cancer qualification or the confirmed cancerquantification.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1A illustrates an exemplary block diagram of a system and networkfor implementing detection tools with digital images, according to anexemplary embodiment of the present disclosure.

FIG. 1B illustrates an exemplary block diagram of a machine learningmodule, according to an exemplary embodiment of the present disclosure.

FIG. 2 is a flowchart illustrating an exemplary method for using adetection machine learning model, according to an exemplary embodimentof the present disclosure.

FIG. 3 illustrates an exemplary block diagram of a training module,according to an exemplary embodiment of the present disclosure.

FIG. 4 illustrates a diagram for detecting cancer cells using adetection module, according to an exemplary embodiment of the presentdisclosure.

FIG. 5 is a flowchart of an exemplary embodiment of a detectionimplementation, according to an exemplary embodiment of the presentdisclosure.

FIG. 6 is a diagram of an experimental result of using a detectionmodel, according to an exemplary embodiment of the present disclosure.

FIG. 7 depicts an example system that may execute techniques presentedherein.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

The systems, devices, and methods disclosed herein are described indetail by way of examples and with reference to the figures. Theexamples discussed herein are examples only and are provided to assistin the explanation of the apparatuses, devices, systems, and methodsdescribed herein. None of the features or components shown in thedrawings or discussed below should be taken as mandatory for anyspecific implementation of any of these devices, systems, or methodsunless specifically designated as mandatory.

Also, for any methods described, regardless of whether the method isdescribed in conjunction with a flow diagram, it should be understoodthat unless otherwise specified or required by context, any explicit orimplicit ordering of steps performed in the execution of a method doesnot imply that those steps must be performed in the order presented butinstead may be performed in a different order or in parallel.

As used herein, the term “exemplary” is used in the sense of “example,”rather than “ideal.” Moreover, the terms “a” and “an” herein do notdenote a limitation of quantity, but rather denote the presence of oneor more of the referenced items. In the discussion that follows,relative terms such as “about,” “substantially,” “approximately,” etc.are used to indicate a possible variation of ±10% or less in a statedvalue, numeric or otherwise.

Pathology refers to the study of diseases. More specifically, pathologyrefers to performing tests and analysis that are used to diagnosediseases. For example, tissue samples may be placed onto slides to beviewed under a microscope by a pathologist (e.g., a physician that is anexpert at analyzing tissue samples to determine whether anyabnormalities exist). That is, pathology specimens may be cut intomultiple sections, stained, and prepared as slides for a pathologist toexamine and render a diagnosis. When uncertain of a diagnostic findingon a slide, a pathologist may order additional cut levels, stains, orother tests to gather more information from the tissue. Technician(s)may then create new slide(s) that may contain the additional informationfor the pathologist to use in making a diagnosis. This process ofcreating additional slides may be time-consuming, not only because itmay involve retrieving the block of tissue, cutting it to make a newslide, and then staining the slide, but also because it may be batchedfor multiple orders. This may significantly delay the final diagnosisthat the pathologist renders. In addition, even after the delay, theremay still be no assurance that the new slide(s) will have informationsufficient to render a diagnosis.

Pathologists may evaluate cancer and other disease pathology slides infor cancer detection. The present disclosure presents an automated wayto identify cancer cells and to make cancer qualifications and, ifapplicable, cancer quantifications. In particular, the presentdisclosure describes various exemplary AI tools that may be integratedinto the workflow to expedite and improve a pathologist's work.

For example, computers may be used to analyze an image of a tissuesample to quickly identify whether the tissue sample includes one ormore cancer cells in order to determine a cancer qualification (e.g.,presence or absence of cancer) as well as a cancer quantification (e.g.,a degree of cancer present). Thus, the process of reviewing stainedslides and tests may be conducted automatically before being reviewed bya pathologist, instead of being reviewed by a pathologist, or inconjunction with being reviewed by a pathologist. When paired withautomatic slide review and cancer detection, this may provide a fullyautomated slide preparation and evaluation pipeline.

Such automation has, at least, the benefits of (1) minimizing an amountof time wasted by a pathologist determining the findings of a slide bymanually detecting cancer cells (2) minimizing the (average total) timefrom specimen acquisition to diagnosis by avoiding the additional timeconducting manual analysis or questionable slides, (3) reducing theamount of repeat tissue evaluation based on missed tissue areas or hardto detect tissue areas (4) reducing the cost of repeated biopsies andpathologist review by accounting for treatment effects, (5) eliminatingor mitigating the need for a second or subsequent pathologist diagnosticreview, (6) reducing the probability of an incorrect diagnosis, (7)increase the probability of a proper diagnosis, and/or (8) identifyingor verifying correct properties (e.g., pCR, MRD, etc.) of a digitalpathology image.

The process of using computers to assist pathologists is calledcomputational pathology. Computing methods used for computationalpathology may include, but are not limited to, statistical analysis,autonomous or machine learning, and AI. AI may include, but is notlimited to, deep learning, neural networks, classifications, clustering,and regression algorithms. By using computational pathology, lives maybe saved by helping pathologists improve their diagnostic accuracy,reliability, efficiency, and accessibility. For example, computationalpathology may be used to assist with detecting slides suspicious forcancer, thereby allowing pathologists to check and confirm their initialassessments before rendering a final diagnosis.

Histopathology refers to the study of a specimen that has been placedonto a slide. For example, a digital pathology image may be comprised ofa digitized image of a microscope slide containing the specimen (e.g., asmear). One method a pathologist may use to analyze an image on a slideis to identify nuclei and classify whether a nucleus is normal (e.g.,benign) or abnormal (e.g., malignant). To assist pathologists inidentifying and classifying nuclei, histological stains may be used tomake cells visible. Many dye-based staining systems have been developed,including periodic acid-Schiff reaction, Masson's trichrome, nissl andmethylene blue, and Haemotoxylin and Eosin (H&E). For medical diagnosis,H&E is a widely used dye-based method, with hematoxylin staining cellnuclei blue, eosin staining cytoplasm and extracellular matrix pink, andother tissue regions taking on variations of these colors. In manycases, however, H&E-stained histologic preparations do not providesufficient information for a pathologist to visually identify biomarkersthat can aid diagnosis or guide treatment. In this situation, techniquessuch as immunohistochemistry (IHC), immunofluorescence, in situhybridization (ISH), or fluorescence in situ hybridization (FISH), maybe used. IHC and immunofluorescence involve, for example, usingantibodies that bind to specific antigens in tissues enabling the visualdetection of cells expressing specific proteins of interest, which canreveal biomarkers that are not reliably identifiable to trainedpathologists based on the analysis of H&E stained slides. ISH and FISHmay be employed to assess the number of copies of genes or the abundanceof specific RNA molecules, depending on the type of probes employed(e.g. DNA probes for gene copy number and RNA probes for the assessmentof RNA expression). If these methods also fail to provide sufficientinformation to detect some biomarkers, genetic testing of the tissue maybe used to confirm if a biomarker is present (e.g., overexpression of aspecific protein or gene product in a tumor, amplification of a givengene in a cancer).

A digitized image may be prepared to show a stained microscope slide,which may allow a pathologist to manually view the image on a slide andestimate a number of stained abnormal cells in the image. However, thisprocess may be time consuming and may lead to errors in identifyingabnormalities because some abnormalities are difficult to detect.Computational processes using machine learning models and devices may beused to assist pathologists in detecting abnormalities that mayotherwise be difficult to detect. For example, AI may be used to detectcancer cells (e.g., as they may be distinguishable from non-cancercells) from salient regions within digital images of tissues stainedusing H&E and other dye-based methods. The images of the tissues couldbe whole slide images (WSI), images of tissue cores within microarraysor selected areas of interest within a tissue section. Using stainingmethods like H&E, these cancer cells may be difficult for humans tovisually detect or quantify without the aid of additional testing. UsingAI to detect these cancer cells from digital images of tissues has thepotential to improve patient care, while also being faster and lessexpensive.

As described above, computational pathology processes and devices of thepresent disclosure may provide an integrated platform allowing a fullyautomated process including data ingestion, processing and viewing ofdigital pathology images via a web-browser or other user interface,while integrating with a laboratory information system (LIS). Further,clinical information may be aggregated using cloud-based data analysisof patient data. The data may come from hospitals, clinics, fieldresearchers, etc., and may be analyzed by machine learning, computervision, natural language processing, and/or statistical algorithms to doreal-time monitoring and forecasting of health patterns at multiplegeographic specificity levels.

Implementations of the disclosed subject matter include systems andmethods for using a detection machine learning model to determine thepresence or absence of cancer cells in a WSI. The detection machinelearning model may be generated to determine a cancer qualification. Thecancer qualification may include an indication of whether cellsrepresented in a digital image of a tissue sample are cancer cells or ifno cancer cells are identified in the digital image. According to animplementation, the cancer qualification may also include a type ofcancer (e.g., breast, prostate, bladder, colorectal, etc.). If a cancerqualification is a confirmed cancer qualification, then a cancerquantification may also be output by the detection machine learningmodel. The cancer quantification may indicate the number, ratio, ordegree of cancer cells identified from the digital image and may be aminimal residual disease (MRD) designation based on an established MRDcriteria (e.g., 1 cell per million or less). If the cancer qualificationoutput by the detection machine learning model indicates no cancercells, a pathological complete response (pCR) cancer qualification maybe output.

The detection machine learning model may be trained based on supervised,semi-supervised, weakly-supervised or un-supervised training includingbut not limited to multiple instance learning. Training images may befrom the same pathology category as the respective digital images inputto the detection machine learning model. According to an implementation,multiple different training images from a plurality of pathologycategories may be used to train the detection machine learning modelacross pathology categories. According to this implementation, an inputto the detection machine learning model may include the pathologycategory of the digital image. Pathology categories may include, but arenot limited to, histology, cytology, frozen section,immunohistochemistry (IHC), immunofluorescence (IF), hematoxylin andeosin (H&E), hematoxylin alone, molecular pathology, 3D imaging, or thelike. The detection machine learning model may be trained to detectcancer cells based on, for example, training images having tagged cancercells. The detection machine learning model may adjust weights in one ormore layers to identify regions likely to have cancer cells based on aknown or determined cancer type and may further adjust weights in one ormore layers based on identifying cancer cells or not finding cancercells within those regions.

According to an implementation, a detection machine learning model maybe trained using training digital images that depict tissue exhibitingtreatment effects. Treatments that may result in treatment effectsinclude, but are not limited to, neoadjuvant therapies such as hormonaltherapies (androgen deprivation therapies (ADT), nonsteroidalantiandrogens (NSAA)), radiation therapy, chemotherapies, or the like.Such treatments may cause treatment damage and change the morphology ofcancerous and benign cells hence making the detection based assessmentsmore challenging than such assessments without treatment effects.Treatment effects may be a result of treatment applied to the patientfrom whom the tissue specimen corresponding to a digital image isobtained. Treatments can often alter the morphology of patient tissue,which is commonly known as “treatment effects,” and can often make theanalysis of determining cancer cells different than an analysis oftissue that does not exhibit treatment effects. The training digitalimages that depict tissue that exhibit treatment effects may or may notbe tagged as being digital images corresponding to tissue havingtreatment effects. A treatment effect machine learning model may betrained based on the images that exhibit treatment effects and may be apart of the detection machine learning model. By utilizing the treatmentdetection machine learning model, the qualification and potentialquantification of cancer by the detection machine learning model may beinformed by the treatment detection machine learning model output andmay provide an indication of the success or failure of a giventreatment. The treatment effect machine learning model may beinitialized by using a base detection machine learning model (i.e., atrained machine learning model trained based on a plurality of trainingimages without treatment effects). Similarly, a pCR and/or MDR detectioncomponent of the detection machine learning model may be initialized byusing a base detection machine learning model.

Notifications, visual indicators, and/or reports may be generated basedon the output of the detection machine learning model. The reports maybe based on an individual digitized image or based on a plurality ofdigitized images either during a given time period or generallyretrospectively.

The systems disclose herein may be implemented locally (e.g.,on-premises) and/or may be remote (e.g., cloud-based). The systems mayor may not have user-interface(s) and workflows that pathologist(s) maydirectly accesses (e.g., a down-stream oncologist could be flagged basedon the cancer qualification or quantification, etc.). Accordingly,implementations disclosed herein may be used as stand-alone operations,or used within a digital workflow.

While the disclosed subject matter is described as implemented based ononcology applications, they may be used for other forms of celldetection (e.g., infectious diseases cells, cystic fibrosis cells,sickle cell anemia, etc.). In addition to providing cancer detectionbenefits, the described implementations may be used for training healthcare professionals (e.g., slide technicians, pathologists, etc.) topractice cell qualification or quantification and/or diagnosisdetermination, while reducing the risk of patient harm.

FIG. 1A illustrates a block diagram of a system and network fordetermining specimen property or image property information pertainingto digital pathology image(s), using machine learning, according to anexemplary embodiment of the present disclosure. As further disclosedherein, the system and network of FIG. 1A may include a machine learningmodule 100 with detection tools 101 to provide a cancer qualificationoutput and, potentially, a cancer quantification output.

Specifically, FIG. 1A illustrates an electronic network 120 that may beconnected to servers at hospitals, laboratories, and/or doctors'offices, etc. For example, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125, etc., may each be connected to an electronicnetwork 120, such as the Internet, through one or more computers,servers, and/or handheld mobile devices. According to an implementation,the electronic network 120 may also be connected to server systems 110,which may include processing devices that are configured to implement amachine learning module 100, in accordance with an exemplary embodimentof the disclosed subject matter.

The physician servers 121, hospital servers 122, clinical trial servers123, research lab servers 124, and/or laboratory information systems 125may create or otherwise obtain images of one or more categories ofpathology specimens including patients' cytology specimen(s),histopathology specimen(s), slide(s) of the cytology specimen(s),histology, immunohistochemistry, immunofluorescence, digitized images ofthe slide(s) of the histopathology specimen(s), or any combinationthereof. The physician servers 121, hospital servers 122, clinical trialservers 123, research lab servers 124, and/or laboratory informationsystems 125 may also obtain any combination of patient-specificinformation, such as age, medical history, cancer treatment history,family history, past biopsy or cytology information, etc. The physicianservers 121, hospital servers 122, clinical trial servers 123, researchlab servers 124, and/or laboratory information systems 125 may transmitdigitized slide images and/or patient-specific information to serversystems 110 over the electronic network 120. Server system(s) 110 mayinclude one or more storage devices 109 for storing images and datareceived from at least one of the physician servers 121, hospitalservers 122, clinical trial servers 123, research lab servers 124,and/or laboratory information systems 125. Server systems 110 may alsoinclude processing devices for processing images and data stored in thestorage devices 109. Server systems 110 may further include one or moremachine learning tool(s) or capabilities via the machine learning module100. For example, the processing devices may include a detection tool101, as shown as machine learning module 100, according to oneembodiment. The detection tool 101 may include a detection machinelearning model, as disclosed herein, as well as one or more othercomponents such as a treatment effects machine learning model,quantification module, or the like. Alternatively or in addition, thepresent disclosure (or portions of the system and methods of the presentdisclosure) may be performed on a local processing device (e.g., alaptop).

The physician servers 121, hospital servers 122, clinical trial servers123, research lab servers 124, and/or laboratory information systems 125refer to systems used by pathologists for reviewing the images of theslides. In hospital settings, tissue type information may be stored in alaboratory information system 125.

FIG. 1B illustrates an exemplary block diagram of a machine learningmodule 100 for determining tissue specimen property or image propertyinformation pertaining to digital pathology image(s), using machinelearning.

Specifically, FIG. 1B depicts components of the machine learning module100, according to one embodiment. For example, the machine learningmodule 100 may include a detection tool 101, a data ingestion tool 102,a slide intake tool 103, a slide scanner 104, a slide manager 105, astorage 106, and a viewing application tool 108. For clarification, themachine learning module 100 shown in FIGS. 1A and 1B is a previouslytrained and generated machine learning model (e.g., a detection machinelearning model that may include a treatment effects machine learningmodel). Additional disclosure is provided herein for training andgenerating different types of machine learning models that may be usedas machine learning module 100.

The detection tool 101 refers to a process and system for determining acancer qualification, and if a confirmed cancer qualification ispresent, determining a cancer quantification. The cancer qualificationmay be a confirmed cancer qualification, a pCR cancer qualification(e.g., no cancer cells detected), or the like. A confirmed cancerqualification may indicate that one or more cancer cells were detectedin the digital image of a tissue specimen. A cancer quantification mayindicate the number of cancer cells detected, a ratio of cancer cells tonon-cancer cells, or a degree of cancer. A subset of the cancerquantification is a MRD cancer qualification which may indicate whetherthe number of cancer cells are below a MRD threshold. The MRD thresholdmay be protocol specific, cancer type specific, institution specific,pathologist specific, or the like. The detection tool 101 may include aplurality of machine learning models or may load one machine learningmodel at a time. For example, the detection tool 101 may include atreatment effects machine learning model that may be trained based on adifferent or additional training data set then the detection machinelearning model disclosed herein.

The data ingestion tool 102 refers to a process and system forfacilitating a transfer of the digital pathology images to the varioustools, modules, components, and devices of the machine learning module100 that are used for characterizing and processing the digitalpathology images, according to an exemplary embodiment.

The slide intake tool 103 refers to a process and system for scanningpathology images and converting them into a digital form, according toan exemplary embodiment. The slides may be scanned with slide scanner104, and the slide manager 105 may process the images on the slides intodigitized pathology images and store the digitized images in storage106.

The viewing application tool 108 refers to a process and system forproviding a user (e.g., pathologist) with a characterization or imageproperty information pertaining to digital pathology images, accordingto an exemplary embodiment. The information may be provided throughvarious output interfaces (e.g., a screen, a monitor, a storage device,and/or a web browser, etc.). As an example, the viewing application tool108 may apply an overlay layer over a digital pathology image and theoverlay layer may highlight key areas of consideration. The overlaylayer may be or may be based on the output of the detection tool 101 ofthe machine learning module 100. As further discussed herein, theviewing application tool 108 may be used to show specific areas of adigital image that correspond to cancer cell or correspond to areas thatcancer cells may be more likely.

The detection tool 101, and each of its components, may transmit and/orreceive digitized slide images and/or patient information to/from serversystems 110, physician servers 121, hospital servers 122, clinical trialservers 123, research lab servers 124, and/or laboratory informationsystems 125 over a network 120. Further, server systems 110 may includestorage devices for storing images and data received from at least oneof the detection tool 101, the data ingestion tool 102, the slide intaketool 103, the slide scanner 104, the slide manager 105, and viewingapplication tool 108. Server systems 110 may also include processingdevices for processing images and data stored in the storage devices.Server systems 110 may further include one or more machine learningtool(s) or capabilities, e.g., due to the processing devices.Alternatively or in addition, the present disclosure (or portions of thesystem and methods of the present disclosure) may be performed on alocal processing device (e.g., a laptop).

The detection tool 101 may provide the output of the machine learningmodule 100 (e.g., a cancer qualification, cancer quantification, pCRqualification, MRD qualification, etc.). As an example, the slide intaketool 103 and the data ingestion tool 102 may receive inputs to themachine learning module 100 and the detection tool 101 may identifycancer cells in the slides based on the data, and output an imagehighlighting the cancer cells or associated areas via the viewingapplication tool 108.

Any of the above devices, tools, and modules may be located on a devicethat may be connected to an electronic network 120, such as the Internetor a cloud service provider, through one or more computers, servers,and/or handheld mobile devices.

FIG. 2 shows a flowchart 200 for outputting cancer qualifications andquantifications based on digital images, in accordance with exemplaryimplementations of the disclosed subject matter. At 202 of FIG. 2, adigital image corresponding to a target specimen associated with apathology category may be received. The digital image may be a digitalpathology image captured using the slide intake tool 103 of FIG. 1B. At204, a detection machine learning model may be determined (e.g., at themachine learning module 100). The detection machine learning model maybe trained by processing a plurality of training images that are fromthe same pathology category as the digital image received at 202. Thepathology categories may include, but are not limited to histology,cytology, frozen section, or immunohistochemistry. According to animplementation, the detection machine learning model may be trainedusing training images from a plurality of different pathologycategories. A digital image corresponding to a target specimen, asreceived at 202, may also be received along with its pathology categorywhich may be provided as an input to the detection machine learningmodel. At 206, the digital image from 202 may be provided to thedetection machine learning model as an input to the model. One or moreother attributes may also be provided as an input to the detectionmachine learning model. The one or more other attributes may include,but are not limited to, a pathology category, a slide type, a glasstype, a tissue type, a tissue region, a chemical used, a stain amount,time applied, scanner type, date, or the like. At 208, the detectionmachine learning model may output a cancer qualification (e.g., a pCRcancer qualification) or a confirmed cancer quantification (e.g., anamount of cancer). According to an implementation, a confirmed cancerqualification (e.g., presence of cancer) may be received in addition toor instead of a confirmed cancer quantification. The cancerqualification may determine if a cancer quantification is generated. Forexample, if a confirmed cancer qualification indicates the presence ofcancer cells then a cancer quantification (e.g., a number of cancercells) may be determined. A qualification indicating a lack of cancercells may not require a cancer quantification unless the cancerqualification threshold is greater than zero cancer cells. At 210, thecancer qualification (e.g., pCR cancer qualification, confirmed cancerqualification, etc.) and, if applicable, a cancer quantification may beoutput as a data signal, a report, a notification, an alert, a visualoutput (e.g., via viewing application tool 108), or the like.

Traditional techniques for detecting cancer cells such as via a manualpathologist review can be subjective and challenging due to thecomplexity of tissue, various attributes of qualification andquantification (e.g., pCR/MRD), definitions of pCR/MRD, and/or treatmenteffects that may change the morphology of cancerous and benign tissuedue to, for example, neoadjuvant therapies. The techniques and systemsbased on the process described in flowchart 200 of FIG. 2 enable robust,objective, and more accurate computational assessment of cancerqualification or quantification including pCR and MRD. MRD and pCRdesignation may be used as a surrogate endpoint for assessment ofdisease-free survival and overall survival to accelerate clinical trials(e.g., for breast cancer). The automated nature of the process describedin FIG. 2 may expedite results and/or treatment approval.

As shown in FIG. 2, a digital image corresponding to a target specimenassociated with a pathology category may be received at 202. The targetspecimen may be a biopsied or otherwise retrieved tissue sampleretrieved from a patient. The target specimen may be retrieved during asurgical procedure where a portion of a patient's tissue is retrievedfrom the patient's body for analysis. The target specimen may be aportion or subset of the total amount of tissue extracted from thepatients such that multiple specimen slides may be generated from thetissue extracted from a single procedure.

The target specimen may be associated with at least one pathologycategory or technique such as histology, cytology, frozen section, H&E,Hematoxylin alone, IHC, molecular pathology, 3D imaging, or the like, asdisclosed herein. According to an implementation, the pathology categoryand other image information about the digital image or target specimenmay also be received. The image information may include, but is notlimited to a slide type, a glass type, a tissue type, a tissue region, achemical used, and a stain amount.

At 204, a detection machine learning model may be determined. Thedetection machine learning model may be trained and generated at themachine learning module 100 or may be trained and generated externallyand be received at the machine learning module 100. The detectionmachine learning model may be trained by processing a plurality oftraining images at least some of which are from the same pathologycategory as the digital image received at 202. The pathology categoriesmay include, but are not limited to histology, cytology, frozen section,H&E, Hematoxylin alone, IHC, molecular pathology, 3D imaging, or thelike. The detection machine learning model may be instantiated using oneor more of deep learning, including but not limited to Deep NeuralNetworks (DNN), Convolutional Neural Networks (CNN), Fully ConvolutionalNetworks (FCN) and Recurrent Neural Networks (RCN), probabilisticmodels, including but not limited to Bayesian Networks and GraphicalModels, and/or discriminative Models, including but not limited toDecision Forests and maximum margin methods, or the like. These modelsmay be trained in supervised, semi-supervised, weakly-supervised orun-supervised fashion such as using multiple instance learning. Aquantification component of the detection machine learning model orseparate machine learning model may also use machine learning techniquesdo generate outputs for quantification of cancer within a digital image(e.g., number of cancer cells within the image). A quantificationcomponent may be trained using, but not limited to, deep learning, CNNs,multiple instance learning, or the like or a combination thereof.

The detection machine learning model may be trained to output cancerqualifications and quantifications, as disclosed herein. Cancerqualifications may be output for one or more of a plurality of differentcancer types. The detection machine learning model may be trained usingimages from the one or more of the plurality of different cancer types.For example, the training images may include images related to breastcancer, prostate cancer, and lung cancer. Accordingly, the generateddetection machine learning model may receive a digital image at 202 ofFIG. 2 and may qualify the image as representing tissue that includescancer cells, the number of cancer cells, and/or the type of cancercells. The detection machine learning model may output the cancerqualification and/or quantification based on weights and/or layerstrained during its training process. Based on the weights and/or layers,the detection machine learning model may identify regions of a digitalimage that may more strongly be used as evidence for the presence orabsence of cancer and, further, the extent of the cancer. The model maythen evaluate some or all of those regions to determine the presence,absence, and/or extent of cancer cells based on training with imagesthat provide the same. Feedback (e.g., pathologist confirmation,correction, adjustment, etc.) may further train the detection machinelearning model during operation of the model.

To generate the detection machine learning model at 204, a trainingdataset including a large plurality of digital pathology images ofpathology specimens (e.g., histology, cytology, frozen section, H&E,Hematoxylin alone, IHC, molecular pathology, 3D imaging, etc.) may beapplied. The digital pathology images may be digital images generatedbased on physical biopsy samples, as disclosed herein, or may be imagesthat are algorithmically generated to replicate tissue specimen (e.g.,human, animal, etc.) by, for example, a rendering system or a generativeadversarial model. Image or specimen associated information (e.g., slidetype, a glass type, a tissue type, a tissue region, a chemical used, astain amount, time applied, scanner type, date, etc.) may also bereceived as part of the training dataset. Additionally, as part oftraining the detection machine learning model, each image may be pairedwith output information about the known or assumed cancer qualificationand, if applicable, cancer quantification. Such output information mayinclude an indication of cancer presence or absence, type of cancer,and/or extent of cancer. The detection machine learning model may learnfrom a plurality of such training images and associated information suchthat the detection machine learning model is trained by modifying one ormore weights based on the qualifications and quantifications associatedwith each training image. Although a supervised training is provided asan example, it will be understood that the training of the detectionmachine learning model may be in supervised, semi-supervised,weakly-supervised or un-supervised.

The training dataset including the digital pathology images, the imageor specimen associated information, and/or the output information may begenerated and/or provided by one or more of the systems 110, physicianservers 121, hospital servers 122, clinical trial servers 123, researchlab servers 124, and/or laboratory information systems 125. Images usedfor training may come from real sources (e.g., humans, animals, etc.) ormay come from synthetic sources (e.g., graphics rendering engines, 3Dmodels, etc.). Examples of digital pathology images may include (a)digitized slides stained with a variety of stains, such as, but notlimited to, H&E, Hematoxylin alone, IHC, molecular pathology, etc.,and/or (b) digitized tissue samples from a 3D imaging device, such asmicroCT.

The detection machine learning model may be generated based on applyingthe digital pathology images with, optionally, the associatedinformation paired with the output information as applied by a machinelearning algorithm. The machine learning algorithm may accept, asinputs, the pathology specimens, the associated information, and theoutput information (e.g., cancer qualifications, cancer quantifications,etc.) and implement training using one or more techniques. For example,the detection machine learning model may be trained in one or more deeplearning algorithms such as, but not limited to, DNN, CNN, FCN, RCN, CNNwith multiple-instance learning or multi-label multiple instancelearning, Recurrent Neural Networks (RNN), Long-short term memory RNN(LSTM), Gated Recurrent Unit RNN (GRU), graph convolution networks, orthe like or a combination thereof. Convolutional neural networks candirectly learn the image feature representations necessary fordiscriminating among characteristics, which can work extremely well whenthere are large amounts of data to train on for each specimen, whereasthe other methods can be used with either traditional computer visionfeatures, e.g., SURF or SIFT, or with learned embeddings (e.g.,descriptors) produced by a trained convolutional neural network, whichcan yield advantages when there are only small amounts of data to train.The trained detection machine learning model may be configured toprovide cancer qualification and/or cancer quantification outputs basedon the digital pathology images.

FIG. 3 shows an example training module 300 to train a detection machinelearning model. As shown in FIG. 3, training data 302 may include one ormore of pathology images 304 (e.g., digital representation of biopsiedimages), input data 306 (e.g., cancer type, pathology category, etc.),and known outcomes 308 (e.g., quality designations) related to thepathology images 304. The training data 302 and a training algorithm 310may be provided to a training component 320 that may apply the trainingdata 302 to the training algorithm 310 in order to generate a detectionmachine learning model.

At 206, the detection machine learning model may be provided an inputincluding a patient based digital pathology image (e.g., a digital imageof pathology specimen (e.g., histology, cytology, immunohistochemistryetc.)) as well as, optionally, associated information. The detectionmachine learning model's internal weights and/or layers may be appliedto the digital pathology image and the associated information todetermine a cancer qualification and, if applicable, a cancerquantification.

A cancer qualification may be a presence or absence of cancer. Thecancer qualification may be a binary determination such detecting asingle cancer cell may correspond to a cancer presence and not detectinga single cancer cell may correspond to an absence of cancer. A pCRcancer qualification may correspond to an absence of cancer and may beoutput when no cancer cells are detected.

According to an implementation, the cancer presence and/or pCR cancerqualification may be protocol specific such that the protocol may defineone or more thresholds for cancer qualification. As an example, aprotocol may dictate that a minimum of five cancer cells per millioncells is required to output presence of cancer and less than five cancercells per million is sufficient for a pCR cancer qualification.

Additionally, as disclosed herein, the detection machine learning modelmay be configured to output a cancer type based on the image datareceived as input. The cancer type output may be a determined cancertype or an indication of a probability of the cancer type. The cancertype output may be informed by inputs in addition to a tissue specimenbased digital image and may include tissue characteristics, slide type,glass type, tissue type, tissue region, chemical used, and/or stainamount.

A cancer quantification may be output when the presence of cancer isdetected. The cancer quantification may include a number of cancer cells(e.g., a number of cancer cells per million), density of cancer cells,or may be an indication that the number of cancer cells is above athreshold amount (e.g., an MRD threshold). For example, the cancerquantification may be or may include a MRD cancer quantification thatmay refer to submicroscopic disease such as disease that remains occultwithin the patient but that may eventually lead to relapse. In cancertreatment, MRD may provide information on whether the treatment hasremoved the cancer or whether traces remain.

The output of the detection machine learning model (i.e., the cancerqualification and, if applicable, cancer quantification), at 210, may beprovided to a storage device 109 of FIG. 1A (e.g., cloud storage, harddrive, network drive, etc.). The output of the detection machinelearning model may be or may also be a notification based on the cancerqualification or cancer quantification. The notification may includeinformation about the cancer qualification, cancer quantification,cancer type, location of cancer cells, or the like. The notification maybe provided via any applicable technique such as a notification signal,a report, a message via an application, a notification via a device, orthe like. The notification may be provided to any applicable device orpersonnel (e.g., histology technician, scanner operator, pathologist,record, etc.). As an example, the output of the detection machinelearning model may be integrated with corresponding target specimen'shistory in, for example, the laboratory information systems 125 thatstores a record of the patient and the associated target specimen.

According to an implementation, the output of the detection machinelearning model may be a report based on the cancer qualification, cancerquantification, cancer type, location of cancer cells, a change overtime in any such factors, or the like. The report may be in anyapplicable format such as a PDF format, HTML format, in-app format, orthe like.

According to an implementation, the output of the detection machinelearning model, at 210, may be or may include a visual indicator. Thevisual indicator may be provided, for example, via the viewingapplication tool 108 of FIG. 1B. The visual indicator may provide avisual representation of the digital image and may provide indicationsregarding the cells used when determining the cancer qualification orcancer quantification.

FIG. 4 shows an example, digital image 400 which is expanded to providean expanded view 400A. The digital image 400 of FIG. 4 is an H&E slidewith treatment effects due to hormonal therapy for prostate cancer. Asshown in the expanded view 400A, an area of cells that is not reliedupon to make a cancer qualification and/or cancer quantification isshown at area 402. Area 402 may correspond to an area that the detectionmachine learning model determined as less relevant for detectionpurposes. Area 402 corresponds to an area that the detection machinelearning model determines as a benign tissue region with no cancer cellsdetected. An area of cells that is relied upon to make a cancerqualification and/or cancer quantification is shown at area 404. Ascompared to the digital image 400 of a tissue specimen, the area 404relied upon to make cancer qualification and/or cancer quantificationmay be relatively small. Area 404 may correspond to an area that thedetection machine learning model determined as more relevant fordetection purposes. Area 404 corresponds to an area that the detectionmachine learning model determines as a cancerous tissue region with atleast one cancer cell detected.

According to an implementation, the detection machine learning algorithmmay also be trained based on and/or receive as inputs clinicalinformation (e.g. patient information, surgical information, diagnosticinformation, etc.), laboratory information (e.g. processing times,personnel, tests, etc.). The detection machine learning algorithm mayprovide cancer qualification and/or cancer quantification based on suchinputs.

According to an implementation, the detection machine learning model mayinclude a treatment effect machine learning model, as disclosed herein.Treatment effects may correspond to oncology treatments based inmedicinal drugs, hormonal therapy, chemotherapy, etc.). Tissue samplesfrom patients treated using a therapy (e.g., cancer therapy) may haveproperties that are different than tissues samples from patients thathave not been treated using similar therapies.

The treatment effect machine learning model may be generated using a lowshot or transfer learning method and may be initialized by using a basedetection machine learning model (i.e., a trained machine learning modeltrained based on a plurality of training images excluding treatmenteffects). For example, a sample detection machine learning model asdisclosed herein may be trained using digital images from tissue samplesfrom patients that have not undergone treatment and/or whose tissuesamples do not exhibit treatment effects. The treatment machine learningmodel may be initialized using the sample detection machine learningmodel such that, weights and/or one or more layers associated with thesample detection machine learning model are kept and additional weights,weight modifications, layers, and/or layer modifications are appliedwhen generating the treatment effect machine learning model. Similarly,a pCR and/or MDR detection component of the detection machine learningmodel may be initialized by using a base detection machine learningmodel.

FIG. 5 shows an example implementation of the detection analysis asdisclosed herein in reference to FIG. 2. FIG. 5 shows a flowchart for aprocess of predicting pCR and/or MRD in prostate cancer with treatmenteffects. In the example of prostate cancer, pCR and MRD may be used asendpoints for clinical trials that assess neoadjuvant treatments.However, a major challenge is the presence of treatment effects inassessing cancer.

At 502, a detection machine learning model may be trained using imagesto output a cancer qualification and, if applicable a cancerquantification if the cancer qualification is a confirmed cancerqualification. The training may include inputting one or more digitalimages of prostate tissue (e.g., histopathology, H&E, IHC, 3D imaging,etc.), including an indication of the presence or absence of cancer. Thedetection training model may be trained using one or more machinelearning algorithms and/or formats, as disclosed herein (e.g., deeplearning, DNN, CNN, FCN, RCN, probabilistic models, discriminativemodels, etc.). The detection learning model may be trained to output thecancer qualification, cancer qualification, as well as an assessment ofpCR and/or MRD which may be protocol specific, as disclosed herein. Thetraining images may include images that may be or may be tagged as beingone of pCR or MRD.

At 504, a quantification module may be trained based on the digitalimages applied at 502. To train the quantification module, aquantification of the amount of prostate cancer (e.g., a number of cellsexhibiting prostate cancer) may be included with all or a subset of thedigital images used to train the model. It will be understood that thequantification module may be part of the overall detection model trainedat 502.

At 506, a treatment effects module may be trained based on the digitalimages applied at 502. All or a subset of the digital images applied at502 may depict tissue exhibiting treatment effects. The treatmenteffects may be inherent in the images or may be tagged such that thetags are used as part of the training.

At 508, a digital image of a pathology sample may be received as aninput to the trained detection machine learning model of 502 as well asone or more of the quantification module of 504 and treatment effectsmodule of 506.

At 510, the detection machine learning model of 502, quantificationmodule of 504, and/or treatment effects module of 506 may be used todetermine a pCR cancer qualification a confirmed cancer qualification.If a pCR cancer qualification is determined, the pCR cancerqualification may be output (e.g., via a notification, report, visualindication, etc.) at 512. If a confirmed cancer qualification isdetermined, the confirmed cancer qualification may be output at 518.Additionally, or alternatively, a cancer quantification may bedetermined. For example, an MRD value may be determined at 514 and maybe output at 516.

FIG. 6 shows an experimental based on the process disclosed in flowchart200 of FIG. 2. FIG. 6 is related to the pathological evaluation ofprostate cancer treated with neoadjuvant hormonal treatment in radicalprostatectomy whole mount sections. Such an evaluation traditionallyposes a significant challenge because of morphologic changes of thetumor combined with the possibility of very small foci of residual tumorin a large volume of non-neoplastic tissue.

A detection machine learning model applied for this experiment mayutilize a multiple instance learning approach to train a whole-slideimage classifier using an SE-ResNet50 convolutional neural network. Themodel, in this example, was trained on 36,644 WSIs (7,514 had cancerousfoci), with a reduced embedding size to accommodate the extremely largenumber of patch instances in a whole mount slide. The detection machinelearning model was then fine-tuned in a fully-supervised context on asmall annotated set of radical prostatectomy specimens retrieved fromprostate cancer patients treated in the neoadjuvant setting to furtherimprove performance on radical prostatectomy data. This produced an AUCof 0.99 on anti-androgen treated cases.

The detection machine learning model showed an AUC of 0.99 in cases withanti-androgen receptor neoadjuvant therapy. After training, this examplesystem was evaluated on 40 WSI images of H&E stained whole mountprostatectomy slides from 15 prostatectomy specimens retrieved frompatients after neoadjuvant treatment with anti-androgen therapy. Groundtruth was established by pathologist annotations. All 37 malignant WSIs(three WSIs contained tumor <5 mm, 34 WSIs contained tumor >5 mm) werecorrectly classified as harboring treated cancer by the system. Of thethree benign WSIs, the detection machine learning model incorrectlyclassified one benign lesion as cancer, while the other two werecorrectly classified as benign tissue.

Accordingly, accurate slide level classification of H&E stained slidesfrom radical prostatectomy specimens has the potential to improveaccuracy and efficiency of histopathologic evaluation of whole mountsections from radical prostatectomy specimens of patients who havereceived neoadjuvant treatment prior to surgery. As shown in FIG. 6, adigital image 600 includes a visual indication of cells that are reliedupon in area 602. An enlarged view 600A shows a specific area 604 withinthe area 602 that more clearly shows the cells relied upon by thedetection machine learning model and also shows cancer cells within area604. A further enlarged view 600B shows a specific area 606 withinspecific area 604 that even more clearly shows the cells relied upon bythe detection machine learning model and also shows cancer cells and/orbenign cells within the area 606.

Device 700, of FIG. 7, may correspond to hardware utilized by serversystems 110, hospital servers 122, research lab servers 124, clinicaltrial servers 123, physician servers 121, laboratory information systems125, and/or client devices, etc. Device 700 may include a centralprocessing unit (CPU) 720. CPU 720 may be any type of processor deviceincluding, for example, any type of special purpose or a general-purposemicroprocessor device. As will be appreciated by persons skilled in therelevant art, CPU 720 also may be a single processor in amulti-core/multiprocessor system, such system operating alone, or in acluster of computing devices operating in a cluster or server farm. CPU720 may be connected to a data communication infrastructure 710, forexample, a bus, message queue, network, or multi-core message-passingscheme.

Device 700 also may include a main memory 740, for example, randomaccess memory (RAM), and may include a secondary memory 730. Secondarymemory 730, e.g., a read-only memory (ROM), may be, for example, a harddisk drive or a removable storage drive. Such a removable storage drivemay comprise, for example, a floppy disk drive, a magnetic tape drive,an optical disk drive, a flash memory, or the like. The removablestorage drive in this example reads from and/or writes to a removablestorage unit in a well-known manner. The removable storage unit maycomprise a floppy disk, magnetic tape, optical disk, etc., which is readby and written to by the removable storage drive. As will be appreciatedby persons skilled in the relevant art, such a removable storage unitgenerally includes a computer usable storage medium having storedtherein computer software and/or data.

In alternative implementations, secondary memory 730 may include othersimilar means for allowing computer programs or other instructions to beloaded into device 700. Examples of such means may include a programcartridge and cartridge interface (such as that found in video gamedevices), a removable memory chip (such as an EPROM, or PROM) andassociated socket, and other removable storage units and interfaces,which allow software and data to be transferred from a removable storageunit to device 700.

Device 700 also may include a communications interface (“COM”) 760.Communications interface 760 allows software and data to be transferredbetween device 700 and external devices. Communications interface 760may include a modem, a network interface (such as an Ethernet card), acommunications port, a PCMCIA slot and card, or the like. Software anddata transferred via communications interface 760 may be in the form ofsignals, which may be electronic, electromagnetic, optical, or othersignals capable of being received by communications interface 760. Thesesignals may be provided to communications interface 760 via acommunications path of device 700, which may be implemented using, forexample, wire or cable, fiber optics, a phone line, a cellular phonelink, an RF link or other communications channels.

The hardware elements, operating systems and programming languages ofsuch equipment are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. Device 700 alsomay include input and output ports 750 to connect with input and outputdevices such as keyboards, mice, touchscreens, monitors, displays, etc.Of course, the various server functions may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load. Alternatively, the servers may be implemented byappropriate programming of one computer hardware platform.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules can be implemented in software,hardware, or a combination of software and hardware.

The tools, modules, and functions described above may be performed byone or more processors. “Storage” type media may include any or all ofthe tangible memory of the computers, processors or the like, orassociated modules thereof, such as various semiconductor memories, tapedrives, disk drives and the like, which may provide non-transitorystorage at any time for software programming.

Software may be communicated through the Internet, a cloud serviceprovider, or other telecommunication networks. For example,communications may enable loading software from one computer orprocessor into another. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

The foregoing general description is exemplary and explanatory only, andnot restrictive of the disclosure. Other embodiments of the inventionwill be apparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. It isintended that the specification and examples be considered as exemplaryonly.

1. A computer-implemented method for processing electronic images, themethod comprising: receiving a digital image corresponding to a targetspecimen collected using a histopathology technique, the target specimenassociated with a pathology category, wherein the digital image is animage of tissue specimen; determining a detection machine learningmodel, the detection machine learning model being generated byprocessing a plurality of training images to output a cancerqualification and further output a cancer quantification if the cancerqualification is an confirmed cancer qualification; providing thedigital image as an input to the detection machine learning model;receiving a pathological complete response (pCR) cancer qualificationwhen the output of the detection machine learning model comprises a pCRcancer qualification and receiving a confirmed cancer quantificationwhen the output of the detection machine learning model comprises aconfirmed cancer quantification; and outputting the pCR cancerqualification or the confirmed cancer quantification.
 2. Thecomputer-implemented method of claim 1, wherein receiving the confirmedcancer quantification comprises receiving a minimal residual disease(MRD) cancer quantification.
 3. The computer-implemented method of claim2, wherein the MRD cancer quantification is protocol specific.
 4. Thecomputer-implemented method of claim 2, wherein the MRD cancerquantification corresponds to a number of cancer cells below a MRDthreshold.
 5. The computer-implemented method of claim 1, wherein thepCR cancer qualification corresponds to the digital image having zerodetectable cancer cells.
 6. The computer-implemented method of claim 1,wherein the plurality of training images comprise images with treatmenteffects.
 7. The computer-implemented method of claim 1, wherein thedetection machine learning model comprises a treatment effect machinelearning model.
 8. The computer-implemented method of claim 7, whereinthe treatment effect machine learning model is initialized by using atrained machine learning model trained based on the plurality oftraining images, the plurality of training images excluding images withtreatment effects.
 9. The computer-implemented method of claim 1,wherein the digital image is from a pathology category, the pathologycategory selected from one or more of histology, cytology, frozensection, immunohistochemistry (IHC), immunofluorescence, hematoxylin andeosin (H&E), hematoxylin alone, molecular pathology, or 3D imaging. 10.A system for processing electronic images, the system comprising: atleast one memory storing instructions; and at least one processorexecuting the instructions to perform operations comprising: receiving adigital image corresponding to a target specimen collected using ahistopathology technique, the target specimen associated with apathology category, wherein the digital image is an image of tissuespecimen; determining a detection machine learning model, the detectionmachine learning model being generated by processing a plurality oftraining images to output a cancer qualification and further output acancer quantification if the cancer qualification is a confirmed cancerqualification; providing the digital image as an input to the detectionmachine learning model wherein the processor is configured to receive apathological complete response (pCR) cancer qualification when theoutput of the detection machine learning model comprises the pCR cancerqualification and also configured to receive a confirmed cancerquantification when the output of the detection machine learning modelcomprises the confirmed cancer quantification; receiving the pCR cancerqualification when the output of the detection machine learning modelcomprises the pCR qualification and receiving the confirmed cancerqualification when the output of the detection machine learning modelcomprises the confirmed cancer qualification; and outputting the pCRcancer qualification or the confirmed cancer qualification.
 11. Thesystem of claim 10, wherein receiving the confirmed cancer qualificationfurther comprises receiving a minimal residual disease (MRD) cancerquantification.
 12. The system of claim 11, wherein the MRD cancerqualification is protocol specific.
 13. The system of claim 10, whereinthe confirmed cancer qualification corresponds to detecting a thresholdnumber of cancer cells.
 14. The system of claim 10, wherein theplurality of training images comprise images with treatment effects. 15.The system of claim 10, wherein the detection machine learning modelcomprises a treatment effect machine learning model.
 16. The system ofclaim 15, wherein the treatment effect machine learning model isinitialized by using a trained machine learning model trained based onthe plurality of training images, the plurality of training imagesexcluding images with treatment effects.
 17. The system of claim 10,wherein the digital image is from a pathology category, the pathologycategory selected from one or more of histology, cytology, frozensection, immunohistochemistry (IHC), immunofluorescence, hematoxylin andeosin (H&E), hematoxylin alone, molecular pathology, or 3D imaging. 18.A non-transitory computer-readable medium storing instructions that,when executed by processor, cause the processor to perform operationsfor processing electronic images, the operations comprising: receiving adigital image corresponding to a target specimen collected using ahistopathology technique, the target specimen associated with apathology category, wherein the digital image is an image of tissuespecimen; determining a detection machine learning model, the detectionmachine learning model being generated by processing a plurality oftraining images to output a cancer qualification and further output acancer quantification if the cancer qualification is a confirmed cancerqualification; providing the digital image as an input to the detectionmachine learning model wherein the processor is configured to receive apathological complete response (pCR) cancer qualification when theoutput of the detection machine learning model comprises a pCR cancerqualification and also configured to receive a confirmed cancerquantification when the output of the detection machine learning modelcomprises a confirmed cancer quantification; receiving the pCR cancerqualification when the output of the detection machine learning modelcomprises the pCR qualification and receiving the confirmed cancerqualification when the output of the detection machine learning modelcomprises the confirmed cancer qualification; and outputting the pCRcancer qualification or the confirmed cancer quantification.
 19. Thenon-transitory computer-readable medium of claim 18, wherein receivingthe confirmed cancer quantification comprises receiving a minimalresidual disease (MRD) cancer quantification.
 20. The non-transitorycomputer-readable medium of claim 18, wherein the detection machinelearning model comprises a treatment effect machine learning model.